[1]罗珊珊[][],李强[][],丁广太[],等. IR-UWB系统中基于能量检测的TOA估计[J].计算机技术与发展,2016,26(11):134-138.
　LUO Shan-shan[][],LI Qiang[][],DING Guang-tai[],et al. TOA Estimation Based on Energy Detection for IR-UWB System[J].,2016,26(11):134-138.
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 IR-UWB系统中基于能量检测的TOA估计()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年11期
页码:
134-138
栏目:
应用开发研究
出版日期:
2016-11-10

文章信息/Info

Title:
 TOA Estimation Based on Energy Detection for IR-UWB System
文章编号:
1673-629X（2016)11-0134-05
作者:
 罗珊珊[1][2];  李强[2][3]; 丁广太[1]; 王营冠[4]; 何为[3]
 1.上海大学 计算机工程与科学学院；2.中国科学院 上海微系统与信息技术研究所；3.上海物联网有限公司
Author(s):
 LUO Shan-shan[1][2];  LI Qiang[2][3];  DING Guang-tai[1] ; WANG Ying-guan[2] ; HE Wei[3]
关键词:
 ; 超带宽; 到达时间估计; 能量检测
Keywords:
 ; UWB ; TOA estimation; energy detection
分类号:
TN925
文献标志码:
A
摘要:
 针对传统能量检测算法中到达时间( TOA)估计精度不高的问题,提出了一种基于MMMR进行归一化门限设置的TC算法。首先,产生符合FCC频率限制的脉冲超宽带(IR-UWB)信号；其次,建立IEEE802.15.4a(CM1,CM2)信道模型,得到接收信号；最后,对所接收信号进行能量分析,得到TOA估计。利用所接收能量块中最大最小值的算术平均值和整个能量采样序列的均值设置MMMR-TC算法的阈值,既包括了噪声,也包括了有用信号。仿真结果表明：MMMR-TC的MAE总体性能均优于经典的MES、MES SB和TC；通过仿真实验确定了能量阈值；特别是在低SNR时,CM1信道下MAE约低于经典TOA估计算法的10%,CM2信道下约为20%；所提算法几乎在所有信噪比范围内都能有效提高TOA估计精度。
Abstract:
 Focused on the issue of the low accuracy of TOA estimation based on energy detection method,a TC ( Threshold Comparison) algorithm is proposed whose normalized threshold is set based on the MMMR ( Maximum and Minimum to Mean energy sample Ratio) criteria of the energy samples. Firstly,the IR-UWB signal is generated that meets the FCC’ s limitation. Secondly,CM1 and CM2 are es-tablished based on the standard IEEE802. 15. 4a channel models. Finally,TOA estimation is gotten by analyzing the energy of received signal. The threshold is computed by the mean energy of the maximum block and the minimum block and the mean energy of all energy block,both with noise and signal. The simulation shows that its overall performance is much better than the classical algorithm like MES ( Maximum Energy Selection)、MES SB ( Maximum Energy Selection-Search Back) and TC. The parameters of threshold are obtained by experiments,especially in the low SNR situation,the proposed algorithm achieves the lower MAE (Mean Absolute Error),which is lower 10% in CM1 and 20% in CM2. The proposed algorithm can effectively improve the TOA estimation accuracy at nearly all SNR ranges.

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更新日期/Last Update: 2016-12-16